Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden — and it does disclose the returned content dimensions rather than just restating the name. A zero-parameter 'get' tool is inherently a safe read, and the description makes clear it reports corpus-wide coverage metrics. It doesn't explicitly state read-only or return format, but the behavior is well conveyed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each earning its place: the first enumerates the returned aggregates, the second gives usage guidance. The scope statement is front-loaded and nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no parameters, the description must describe what comes back — and it enumerates the metric categories thoroughly. An agent can decide to call this and know what to expect without further information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4; there is no argument surface for the description to clarify. No gaps exist here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (aggregate statistics for the entire BuildFeed corpus) and enumerates exactly which aggregates — total videos, channels, categories, topics, views, date range, enrichment coverage. It also distinguishes itself from per-video siblings like get_video.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit condition for use: high-level 'how much data / how fresh' questions, contrasted against 'per-video detail'. It stops short of naming the specific alternative tools (get_video, list_videos), but the routing intent is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.